# coding=utf-8
# Copyright 2023 Meta AI and The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""PyTorch DINOv2 model."""

import collections.abc
from typing import Callable, Optional, Union

import torch
from torch import nn

from ...activations import ACT2FN
from ...modeling_layers import GradientCheckpointingLayer
from ...modeling_outputs import BackboneOutput, BaseModelOutput, BaseModelOutputWithPooling, ImageClassifierOutput
from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
from ...processing_utils import Unpack
from ...pytorch_utils import find_pruneable_heads_and_indices, prune_linear_layer
from ...utils import TransformersKwargs, auto_docstring, logging, torch_int
from ...utils.backbone_utils import BackboneMixin
from ...utils.generic import can_return_tuple, check_model_inputs
from .configuration_dinov2 import Dinov2Config


logger = logging.get_logger(__name__)


class Dinov2Embeddings(nn.Module):
    """
    Construct the CLS token, mask token, position and patch embeddings.
    """

    def __init__(self, config: Dinov2Config) -> None:
        super().__init__()

        self.cls_token = nn.Parameter(torch.randn(1, 1, config.hidden_size))
        if config.use_mask_token:
            self.mask_token = nn.Parameter(torch.zeros(1, config.hidden_size))
        self.patch_embeddings = Dinov2PatchEmbeddings(config)
        num_patches = self.patch_embeddings.num_patches
        self.position_embeddings = nn.Parameter(torch.randn(1, num_patches + 1, config.hidden_size))
        self.dropout = nn.Dropout(config.hidden_dropout_prob)
        self.patch_size = config.patch_size
        self.use_mask_token = config.use_mask_token
        self.config = config

    def interpolate_pos_encoding(self, embeddings: torch.Tensor, height: int, width: int) -> torch.Tensor:
        """
        This method allows to interpolate the pre-trained position encodings, to be able to use the model on higher resolution
        images. This method is also adapted to support torch.jit tracing and interpolation at torch.float32 precision.

        Adapted from:
        - https://github.com/facebookresearch/dino/blob/de9ee3df6cf39fac952ab558447af1fa1365362a/vision_transformer.py#L174-L194, and
        - https://github.com/facebookresearch/dinov2/blob/e1277af2ba9496fbadf7aec6eba56e8d882d1e35/dinov2/models/vision_transformer.py#L179-L211
        """

        num_patches = embeddings.shape[1] - 1
        num_positions = self.position_embeddings.shape[1] - 1

        # always interpolate when tracing to ensure the exported model works for dynamic input shapes
        if not torch.jit.is_tracing() and num_patches == num_positions and height == width:
            return self.position_embeddings

        class_pos_embed = self.position_embeddings[:, :1]
        patch_pos_embed = self.position_embeddings[:, 1:]

        dim = embeddings.shape[-1]

        new_height = height // self.patch_size
        new_width = width // self.patch_size

        sqrt_num_positions = torch_int(num_positions**0.5)
        patch_pos_embed = patch_pos_embed.reshape(1, sqrt_num_positions, sqrt_num_positions, dim)
        patch_pos_embed = patch_pos_embed.permute(0, 3, 1, 2)
        target_dtype = patch_pos_embed.dtype
        patch_pos_embed = nn.functional.interpolate(
            patch_pos_embed.to(torch.float32),
            size=(new_height, new_width),
            mode="bicubic",
            align_corners=False,
        ).to(dtype=target_dtype)

        patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim)

        return torch.cat((class_pos_embed, patch_pos_embed), dim=1)

    def forward(self, pixel_values: torch.Tensor, bool_masked_pos: Optional[torch.Tensor] = None) -> torch.Tensor:
        batch_size, _, height, width = pixel_values.shape
        target_dtype = self.patch_embeddings.projection.weight.dtype
        embeddings = self.patch_embeddings(pixel_values.to(dtype=target_dtype))

        if bool_masked_pos is not None and self.use_mask_token:
            embeddings = torch.where(
                bool_masked_pos.unsqueeze(-1), self.mask_token.to(embeddings.dtype).unsqueeze(0), embeddings
            )

        # add the [CLS] token to the embedded patch tokens
        cls_tokens = self.cls_token.expand(batch_size, -1, -1)
        embeddings = torch.cat((cls_tokens, embeddings), dim=1)

        # add positional encoding to each token
        embeddings = embeddings + self.interpolate_pos_encoding(embeddings, height, width)

        embeddings = self.dropout(embeddings)

        return embeddings


class Dinov2PatchEmbeddings(nn.Module):
    """
    This class turns `pixel_values` of shape `(batch_size, num_channels, height, width)` into the initial
    `hidden_states` (patch embeddings) of shape `(batch_size, seq_length, hidden_size)` to be consumed by a
    Transformer.
    """

    def __init__(self, config):
        super().__init__()
        image_size, patch_size = config.image_size, config.patch_size
        num_channels, hidden_size = config.num_channels, config.hidden_size

        image_size = image_size if isinstance(image_size, collections.abc.Iterable) else (image_size, image_size)
        patch_size = patch_size if isinstance(patch_size, collections.abc.Iterable) else (patch_size, patch_size)
        num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
        self.image_size = image_size
        self.patch_size = patch_size
        self.num_channels = num_channels
        self.num_patches = num_patches

        self.projection = nn.Conv2d(num_channels, hidden_size, kernel_size=patch_size, stride=patch_size)

    def forward(self, pixel_values: torch.Tensor) -> torch.Tensor:
        num_channels = pixel_values.shape[1]
        if num_channels != self.num_channels:
            raise ValueError(
                "Make sure that the channel dimension of the pixel values match with the one set in the configuration."
                f" Expected {self.num_channels} but got {num_channels}."
            )
        embeddings = self.projection(pixel_values).flatten(2).transpose(1, 2)
        return embeddings


# Copied from transformers.models.vit.modeling_vit.eager_attention_forward
def eager_attention_forward(
    module: nn.Module,
    query: torch.Tensor,
    key: torch.Tensor,
    value: torch.Tensor,
    attention_mask: Optional[torch.Tensor],
    scaling: float,
    dropout: float = 0.0,
    **kwargs,
):
    # Take the dot product between "query" and "key" to get the raw attention scores.
    attn_weights = torch.matmul(query, key.transpose(-1, -2)) * scaling

    # Normalize the attention scores to probabilities.
    attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)

    # This is actually dropping out entire tokens to attend to, which might
    # seem a bit unusual, but is taken from the original Transformer paper.
    attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)

    # Mask heads if we want to
    if attention_mask is not None:
        attn_weights = attn_weights * attention_mask

    attn_output = torch.matmul(attn_weights, value)
    attn_output = attn_output.transpose(1, 2).contiguous()

    return attn_output, attn_weights


# Copied from transformers.models.vit.modeling_vit.ViTSelfAttention with ViT->Dinov2
class Dinov2SelfAttention(nn.Module):
    def __init__(self, config: Dinov2Config):
        super().__init__()
        if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
            raise ValueError(
                f"The hidden size {config.hidden_size} is not a multiple of the number of attention "
                f"heads {config.num_attention_heads}."
            )

        self.config = config
        self.num_attention_heads = config.num_attention_heads
        self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
        self.all_head_size = self.num_attention_heads * self.attention_head_size
        self.dropout_prob = config.attention_probs_dropout_prob
        self.scaling = self.attention_head_size**-0.5
        self.is_causal = False

        self.query = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias)
        self.key = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias)
        self.value = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias)

    def forward(
        self, hidden_states: torch.Tensor, head_mask: Optional[torch.Tensor] = None
    ) -> tuple[torch.Tensor, torch.Tensor]:
        batch_size = hidden_states.shape[0]
        new_shape = batch_size, -1, self.num_attention_heads, self.attention_head_size

        key_layer = self.key(hidden_states).view(*new_shape).transpose(1, 2)
        value_layer = self.value(hidden_states).view(*new_shape).transpose(1, 2)
        query_layer = self.query(hidden_states).view(*new_shape).transpose(1, 2)

        attention_interface: Callable = eager_attention_forward
        if self.config._attn_implementation != "eager":
            attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]

        context_layer, attention_probs = attention_interface(
            self,
            query_layer,
            key_layer,
            value_layer,
            head_mask,
            is_causal=self.is_causal,
            scaling=self.scaling,
            dropout=0.0 if not self.training else self.dropout_prob,
        )

        new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
        context_layer = context_layer.reshape(new_context_layer_shape)

        return context_layer, attention_probs


# Copied from transformers.models.vit.modeling_vit.ViTSelfOutput with ViT->Dinov2
class Dinov2SelfOutput(nn.Module):
    """
    The residual connection is defined in Dinov2Layer instead of here (as is the case with other models), due to the
    layernorm applied before each block.
    """

    def __init__(self, config: Dinov2Config):
        super().__init__()
        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)

    def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
        hidden_states = self.dense(hidden_states)
        hidden_states = self.dropout(hidden_states)
        return hidden_states


# Copied from transformers.models.vit.modeling_vit.ViTAttention with ViT->Dinov2
class Dinov2Attention(nn.Module):
    def __init__(self, config: Dinov2Config):
        super().__init__()
        self.attention = Dinov2SelfAttention(config)
        self.output = Dinov2SelfOutput(config)
        self.pruned_heads = set()

    def prune_heads(self, heads: set[int]):
        if len(heads) == 0:
            return
        heads, index = find_pruneable_heads_and_indices(
            heads, self.attention.num_attention_heads, self.attention.attention_head_size, self.pruned_heads
        )

        # Prune linear layers
        self.attention.query = prune_linear_layer(self.attention.query, index)
        self.attention.key = prune_linear_layer(self.attention.key, index)
        self.attention.value = prune_linear_layer(self.attention.value, index)
        self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)

        # Update hyper params and store pruned heads
        self.attention.num_attention_heads = self.attention.num_attention_heads - len(heads)
        self.attention.all_head_size = self.attention.attention_head_size * self.attention.num_attention_heads
        self.pruned_heads = self.pruned_heads.union(heads)

    def forward(self, hidden_states: torch.Tensor, head_mask: Optional[torch.Tensor] = None) -> torch.Tensor:
        self_attn_output, _ = self.attention(hidden_states, head_mask)
        output = self.output(self_attn_output, hidden_states)
        return output


class Dinov2LayerScale(nn.Module):
    def __init__(self, config) -> None:
        super().__init__()
        self.lambda1 = nn.Parameter(config.layerscale_value * torch.ones(config.hidden_size))

    def forward(self, hidden_state: torch.Tensor) -> torch.Tensor:
        return hidden_state * self.lambda1


# Copied from transformers.models.beit.modeling_beit.drop_path
def drop_path(input: torch.Tensor, drop_prob: float = 0.0, training: bool = False) -> torch.Tensor:
    """
    Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).

    """
    if drop_prob == 0.0 or not training:
        return input
    keep_prob = 1 - drop_prob
    shape = (input.shape[0],) + (1,) * (input.ndim - 1)  # work with diff dim tensors, not just 2D ConvNets
    random_tensor = keep_prob + torch.rand(shape, dtype=input.dtype, device=input.device)
    random_tensor.floor_()  # binarize
    output = input.div(keep_prob) * random_tensor
    return output


# Copied from transformers.models.beit.modeling_beit.BeitDropPath
class Dinov2DropPath(nn.Module):
    """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks)."""

    def __init__(self, drop_prob: Optional[float] = None) -> None:
        super().__init__()
        self.drop_prob = drop_prob

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        return drop_path(hidden_states, self.drop_prob, self.training)

    def extra_repr(self) -> str:
        return f"p={self.drop_prob}"


class Dinov2MLP(nn.Module):
    def __init__(self, config) -> None:
        super().__init__()
        in_features = out_features = config.hidden_size
        hidden_features = int(config.hidden_size * config.mlp_ratio)
        self.fc1 = nn.Linear(in_features, hidden_features, bias=True)
        if isinstance(config.hidden_act, str):
            self.activation = ACT2FN[config.hidden_act]
        else:
            self.activation = config.hidden_act
        self.fc2 = nn.Linear(hidden_features, out_features, bias=True)

    def forward(self, hidden_state: torch.Tensor) -> torch.Tensor:
        hidden_state = self.fc1(hidden_state)
        hidden_state = self.activation(hidden_state)
        hidden_state = self.fc2(hidden_state)
        return hidden_state


class Dinov2SwiGLUFFN(nn.Module):
    def __init__(self, config) -> None:
        super().__init__()
        in_features = out_features = config.hidden_size
        hidden_features = int(config.hidden_size * config.mlp_ratio)
        hidden_features = (int(hidden_features * 2 / 3) + 7) // 8 * 8

        self.weights_in = nn.Linear(in_features, 2 * hidden_features, bias=True)
        self.weights_out = nn.Linear(hidden_features, out_features, bias=True)

    def forward(self, hidden_state: torch.Tensor) -> torch.Tensor:
        hidden_state = self.weights_in(hidden_state)
        x1, x2 = hidden_state.chunk(2, dim=-1)
        hidden = nn.functional.silu(x1) * x2
        return self.weights_out(hidden)


class Dinov2Layer(GradientCheckpointingLayer):
    """This corresponds to the Block class in the original implementation."""

    def __init__(self, config: Dinov2Config) -> None:
        super().__init__()

        self.norm1 = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        self.attention = Dinov2Attention(config)
        self.layer_scale1 = Dinov2LayerScale(config)
        self.drop_path = Dinov2DropPath(config.drop_path_rate) if config.drop_path_rate > 0.0 else nn.Identity()

        self.norm2 = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)

        if config.use_swiglu_ffn:
            self.mlp = Dinov2SwiGLUFFN(config)
        else:
            self.mlp = Dinov2MLP(config)
        self.layer_scale2 = Dinov2LayerScale(config)

    def forward(
        self,
        hidden_states: torch.Tensor,
        head_mask: Optional[torch.Tensor] = None,
    ) -> torch.Tensor:
        hidden_states_norm = self.norm1(hidden_states)
        self_attention_output = self.attention(hidden_states_norm, head_mask)
        self_attention_output = self.layer_scale1(self_attention_output)

        # first residual connection
        hidden_states = self.drop_path(self_attention_output) + hidden_states

        # in Dinov2, layernorm is also applied after self-attention
        layer_output = self.norm2(hidden_states)
        layer_output = self.mlp(layer_output)
        layer_output = self.layer_scale2(layer_output)

        # second residual connection
        layer_output = self.drop_path(layer_output) + hidden_states

        return layer_output


class Dinov2Encoder(nn.Module):
    def __init__(self, config: Dinov2Config):
        super().__init__()
        self.config = config
        self.layer = nn.ModuleList([Dinov2Layer(config) for _ in range(config.num_hidden_layers)])
        self.gradient_checkpointing = False

    def forward(
        self, hidden_states: torch.Tensor, head_mask: Optional[torch.Tensor] = None, output_hidden_states: bool = False
    ) -> BaseModelOutput:
        all_hidden_states = [hidden_states] if output_hidden_states else None
        for i, layer_module in enumerate(self.layer):
            layer_head_mask = head_mask[i] if head_mask is not None else None
            hidden_states = layer_module(hidden_states, layer_head_mask)
            if all_hidden_states:
                all_hidden_states.append(hidden_states)

        return BaseModelOutput(
            last_hidden_state=hidden_states,
            hidden_states=tuple(all_hidden_states) if all_hidden_states else None,
        )


@auto_docstring
class Dinov2PreTrainedModel(PreTrainedModel):
    config: Dinov2Config
    base_model_prefix = "dinov2"
    main_input_name = "pixel_values"
    supports_gradient_checkpointing = True
    _no_split_modules = ["Dinov2Layer"]
    _supports_sdpa = True
    _supports_flash_attn = True
    _supports_flex_attn = True
    _supports_attention_backend = True
    _can_record_outputs = {
        "attentions": Dinov2SelfAttention,
    }

    def _init_weights(self, module: Union[nn.Linear, nn.Conv2d, nn.LayerNorm]) -> None:
        """Initialize the weights"""
        if isinstance(module, (nn.Linear, nn.Conv2d)):
            # Upcast the input in `fp32` and cast it back to desired `dtype` to avoid
            # `trunc_normal_cpu` not implemented in `half` issues
            module.weight.data = nn.init.trunc_normal_(
                module.weight.data.to(torch.float32), mean=0.0, std=self.config.initializer_range
            ).to(module.weight.dtype)
            if module.bias is not None:
                module.bias.data.zero_()
        elif isinstance(module, nn.LayerNorm):
            module.bias.data.zero_()
            module.weight.data.fill_(1.0)
        elif isinstance(module, Dinov2Embeddings):
            module.position_embeddings.data = nn.init.trunc_normal_(
                module.position_embeddings.data.to(torch.float32),
                mean=0.0,
                std=self.config.initializer_range,
            ).to(module.position_embeddings.dtype)

            module.cls_token.data = nn.init.trunc_normal_(
                module.cls_token.data.to(torch.float32),
                mean=0.0,
                std=self.config.initializer_range,
            ).to(module.cls_token.dtype)

            if self.config.use_mask_token:
                module.mask_token.data.zero_()
        elif isinstance(module, Dinov2LayerScale):
            module.lambda1.data.fill_(self.config.layerscale_value)


@auto_docstring
class Dinov2Model(Dinov2PreTrainedModel):
    def __init__(self, config: Dinov2Config):
        super().__init__(config)
        self.config = config

        self.embeddings = Dinov2Embeddings(config)
        self.encoder = Dinov2Encoder(config)

        self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)

        # Initialize weights and apply final processing
        self.post_init()

    def get_input_embeddings(self) -> Dinov2PatchEmbeddings:
        return self.embeddings.patch_embeddings

    def _prune_heads(self, heads_to_prune: dict[int, list[int]]) -> None:
        """
        Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
        class PreTrainedModel
        """
        for layer, heads in heads_to_prune.items():
            self.encoder.layer[layer].attention.prune_heads(heads)

    @check_model_inputs
    @auto_docstring
    def forward(
        self,
        pixel_values: Optional[torch.Tensor] = None,
        bool_masked_pos: Optional[torch.Tensor] = None,
        head_mask: Optional[torch.Tensor] = None,
        output_hidden_states: Optional[bool] = None,
        **kwargs,
    ) -> BaseModelOutputWithPooling:
        r"""
        bool_masked_pos (`torch.BoolTensor` of shape `(batch_size, sequence_length)`):
            Boolean masked positions. Indicates which patches are masked (1) and which aren't (0). Only relevant for
            pre-training.
        """
        if output_hidden_states is None:
            output_hidden_states = self.config.output_hidden_states

        if pixel_values is None:
            raise ValueError("You have to specify pixel_values")

        # Prepare head mask if needed
        # 1.0 in head_mask indicate we keep the head
        # attention_probs has shape bsz x n_heads x N x N
        # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
        # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
        head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)

        embedding_output = self.embeddings(pixel_values, bool_masked_pos=bool_masked_pos)

        encoder_outputs: BaseModelOutput = self.encoder(
            embedding_output, head_mask=head_mask, output_hidden_states=output_hidden_states
        )
        sequence_output = encoder_outputs.last_hidden_state
        sequence_output = self.layernorm(sequence_output)
        pooled_output = sequence_output[:, 0, :]

        return BaseModelOutputWithPooling(
            last_hidden_state=sequence_output,
            pooler_output=pooled_output,
            hidden_states=encoder_outputs.hidden_states,
        )


@auto_docstring(
    custom_intro="""
    Dinov2 Model transformer with an image classification head on top (a linear layer on top of the final hidden state
    of the [CLS] token) e.g. for ImageNet.
    """
)
class Dinov2ForImageClassification(Dinov2PreTrainedModel):
    def __init__(self, config: Dinov2Config) -> None:
        super().__init__(config)

        self.num_labels = config.num_labels
        self.dinov2 = Dinov2Model(config)

        # Classifier head
        self.classifier = (
            nn.Linear(config.hidden_size * 2, config.num_labels) if config.num_labels > 0 else nn.Identity()
        )

        # Initialize weights and apply final processing
        self.post_init()

    @can_return_tuple
    @auto_docstring
    def forward(
        self,
        pixel_values: Optional[torch.Tensor] = None,
        head_mask: Optional[torch.Tensor] = None,
        labels: Optional[torch.Tensor] = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> ImageClassifierOutput:
        r"""
        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
            Labels for computing the image classification/regression loss. Indices should be in `[0, ...,
            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
        """
        outputs: BaseModelOutputWithPooling = self.dinov2(pixel_values, head_mask=head_mask, **kwargs)

        sequence_output = outputs.last_hidden_state  # batch_size, sequence_length, hidden_size
        cls_token = sequence_output[:, 0]
        patch_tokens = sequence_output[:, 1:]

        linear_input = torch.cat([cls_token, patch_tokens.mean(dim=1)], dim=1)
        logits = self.classifier(linear_input)

        loss = None
        if labels is not None:
            loss = self.loss_function(labels, logits, self.config, **kwargs)

        return ImageClassifierOutput(
            loss=loss,
            logits=logits,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )


@auto_docstring(
    custom_intro="""
    Dinov2 backbone, to be used with frameworks like DETR and MaskFormer.
    """
)
class Dinov2Backbone(Dinov2PreTrainedModel, BackboneMixin):
    def __init__(self, config):
        super().__init__(config)
        super()._init_backbone(config)

        self.num_features = [config.hidden_size for _ in range(config.num_hidden_layers + 1)]
        self.embeddings = Dinov2Embeddings(config)
        self.encoder = Dinov2Encoder(config)

        self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)

        # Initialize weights and apply final processing
        self.post_init()

    def get_input_embeddings(self) -> Dinov2PatchEmbeddings:
        return self.embeddings.patch_embeddings

    @check_model_inputs
    @auto_docstring
    def forward(
        self, pixel_values: torch.Tensor, output_hidden_states: Optional[bool] = None, **kwargs
    ) -> BackboneOutput:
        r"""
        Examples:

        ```python
        >>> from transformers import AutoImageProcessor, AutoBackbone
        >>> import torch
        >>> from PIL import Image
        >>> import requests

        >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
        >>> image = Image.open(requests.get(url, stream=True).raw)

        >>> processor = AutoImageProcessor.from_pretrained("facebook/dinov2-base")
        >>> model = AutoBackbone.from_pretrained(
        ...     "facebook/dinov2-base", out_features=["stage2", "stage5", "stage8", "stage11"]
        ... )

        >>> inputs = processor(image, return_tensors="pt")

        >>> outputs = model(**inputs)
        >>> feature_maps = outputs.feature_maps
        >>> list(feature_maps[-1].shape)
        [1, 768, 16, 16]
        ```"""
        if output_hidden_states is None:
            output_hidden_states = self.config.output_hidden_states

        embedding_output = self.embeddings(pixel_values)
        output: BaseModelOutput = self.encoder(embedding_output, output_hidden_states=True)
        hidden_states = output.hidden_states

        feature_maps = []
        for stage, hidden_state in zip(self.stage_names, hidden_states):
            if stage in self.out_features:
                if self.config.apply_layernorm:
                    hidden_state = self.layernorm(hidden_state)
                if self.config.reshape_hidden_states:
                    hidden_state = hidden_state[:, 1:]
                    # this was actually a bug in the original implementation that we copied here,
                    # cause normally the order is height, width
                    batch_size, _, height, width = pixel_values.shape
                    patch_size = self.config.patch_size
                    hidden_state = hidden_state.reshape(batch_size, height // patch_size, width // patch_size, -1)
                    hidden_state = hidden_state.permute(0, 3, 1, 2).contiguous()
                feature_maps.append(hidden_state)

        return BackboneOutput(
            feature_maps=tuple(feature_maps),
            hidden_states=hidden_states if output_hidden_states else None,
        )


__all__ = ["Dinov2ForImageClassification", "Dinov2Model", "Dinov2PreTrainedModel", "Dinov2Backbone"]
